The publication of Issue 4 marks the completion of Volume 8 of Learning Health Systems. An international, trans-disciplinary, open access publication, the journal has advanced research and scholarship on learning health systems in partnership with our reviewers. With indexing in multiple major sources and an Impact Factor of 2.6, we have achieved a publication milestone that signals a sustainable, positive trajectory. Articles from the journal were downloaded over 123, 126 times in 2023.
Each year, the journal publishes a Special Issue; we have now published eight Special Issues: “Patient Empowerment and the Learning Health System” (v.1); “Ethical, Legal, and Social Implications of Learning Health Systems” (v.2); “Learning Health Systems: Connecting Research to Practice Worldwide” (v.3); “Human Phenomics and the Learning Health System” (v.4); “Collaborative Learning Health Systems: Science and Practice” (v.5); and “Education To Meet the Multidisciplinary Workforce Needs of Learning Health Systems” (v.6); “Transforming Health Through Computable Biomedical Knowledge (CBK)” (v.7); and “Envisioning Public Health As a Learning Health System” (v.8). Our talented guest editors have been instrumental in helping these Special Issues come to fruition.
In addition, we published a Supplement (“Focus on Research by AcademyHealth members”) in June 2024. The Supplement was a collaboration with the Department of Learning Health Sciences (University of Michigan), Academy Health, (LHS Interest Group), and John Wiley & Sons.
We are keenly aware that these achievements would not have happened without the dedicated efforts and insightful comments of all those individuals who accepted invitations to review submitted articles. With busy schedules and full commitments, these individuals found the time and energy to contribute their expertise to our authors to help ensure that their papers met (and often exceeded) the journal's high standards for publication.
Please accept our sincere gratitude for your outstanding efforts!
Charles P. Friedman, Editor in Chief
Chronic diseases are a considerable burden to health systems, communities, and patients. Much of this burden, however, could be prevented if interventions effective in reducing chronic disease risks were routinely implemented.
The aim of this paper is to discuss the role of public health agencies in preventing chronic disease through the application of learning health system (LHS) approaches to improve the implementation of evidence-based interventions.
We draw on the literature and our experience operating a local LHS in Australia that has achieved rapid improvements in the implementation of chronic disease prevention interventions.
The proposed LHS framework has been adapted to be both implementation and chronic disease prevention focused. The framework describes both broad improvement processes, and the infrastructure and other support (pillars) recommended to support its core functions.
The framework serves as a basis for further exploration of the potentially transformative role LHS's may have in addressing the chronic disease health crisis.
Learning Health Systems (LHSs) have not directly informed evidence-based policymaking. The Translation-to-Policy (T2P) Learning Cycle aligns scientists, end-users, and policymakers to support a repeatable roadmap of innovation and quality improvement to optimize effective policies toward a common public health goal. We describe T2P learning cycle components and provide examples of their application.
The T2P Learning Cycle is based on the U.S. Department of Veterans Affairs (VA) Office of Research and Development and Quality Enhancement Research Initiative (QUERI), which supports research and quality improvement addressing national public health priorities to inform policy and ensure programs are evidence-based and work for end-users. Incorporating LHS infrastructure, the T2P Learning Cycle is responsive to the Foundations for Evidence-based Policymaking Act, which requires U.S. government agencies to justify budgets using evidence.
The learning community (patients, providers, clinical/policy leaders, and investigators) drives the T2P Learning Cycle, working toward one or more specific, shared priority goals, and supports a repeatable cycle of evidence-building and evaluation. Core T2P Learning Cycle functions observed in the examples from housing/economic security, precision oncology, and aging include governance and standard operating procedures to promote effective priority-setting; complementary research and quality improvement initiatives, which inform ongoing data curation at the learning system level; and sustainment of continuous improvement and evidence-based policymaking.
The T2P Learning Cycle integrates research translation with evidence-based policymaking, ensuring that scientific innovations address public health priorities and serve end-users through a repeatable process of research and quality improvement that ensures policies are scientifically based, effective, and sustainable.
Policies aiming to prevent ill health and reduce health inequalities need to consider the full complexity of health systems, including environmental determinants. A learning health system that incorporates environmental factors needs healthcare, social care and non-health data linkage at individual and small-area levels. Our objective was to establish privacy-preserving household record linkage for England to ensure person-level data remain secure and private when linked with data from households or the wider environment.
A stakeholder workshop with participants from our regional health board, together with the regional data processor, and the national data provider. The workshop discussed the risks and benefits of household linkages. This group then co-designed actionable dataflows between national and local data controllers and processors.
A process was defined whereby the Personal Demographics Service, which includes the addresses of all patients of the National Health Service (NHS) in England, was used to match patients to a home identifier, for the time they are recorded as living at that address. Discussions with NHS England resulted in secure and quality-assured data linkages and a plan to flow these pseudonymised data onwards into regional health boards. Methods were established, including the generation of matching algorithms, transfer processes and information governance approvals. Our collaboration accelerated the development of a new data governance application, facilitating future public health intervention evaluations.
These activities have established a secure method for protecting the privacy of NHS patients in England, while allowing linkage of wider environmental data. This enables local health systems to learn from their data and improve health by optimizing non-health factors. Proportionate governance of health and linked non-health data is practical in England for incorporating key environmental factors into a learning health system.
Public health systems worldwide face increasing challenges in addressing complex health issues and improving population health outcomes. This experience report introduces the concept of a Learning Public Health System (LPHS) as a potential solution to transform public health practice. Building upon the framework of a Learning Health System (LHS) in healthcare, the LPHS aims to create a dynamic, data-driven ecosystem that continuously improves public health interventions and policies. This report explores the definition, benefits, challenges, and implementation strategies of an LPHS, highlighting its potential to revolutionize public health practice.
This report employs a comparative analysis approach, examining the similarities and differences between an LPHS and an LHS. It also identifies and elaborates on the potential benefits, challenges, and barriers to implementing an LPHS. Additionally, the study investigates promising national initiatives that exemplify elements of an LPHS in action.
An LPHS integrates data from diverse sources to inform knowledge generation, policy development, and operational improvements. Key benefits of implementing an LPHS include improved disease prevention, evidence-informed policy-making, and enhanced health outcomes. However, several challenges were identified, such as interoperability issues, governance concerns, funding limitations, and cultural factors that may impede the widespread adoption of an LPHS.
Implementation of an LPHS has the potential to significantly transform public health practice. To realize this potential, a call to action is issued for stakeholders across the public health ecosystem. Recommendations include investing in informatics infrastructure, prioritizing workforce development, establishing robust data governance frameworks, and creating incentives to support the development and implementation of a LPHS. By addressing these key areas, public health systems can evolve to become more responsive, efficient, and effective in improving population health outcomes.
Publishing medical metadata stored in case report forms (CRFs) is a prerequisite for the development of a learning health system (LHS) by fostering reuse of metadata and standardization in health research. The aim of our study was to investigate medical researchers' (MRs) willingness to share CRFs, to identify reasons for and against CRF sharing, and to determine if and under which conditions MRs might consider sharing CRF metadata via a public registry.
We examined CRF data sharing commitments for 1842 interventional trials registered on the German Clinical Trials Registry (DRKS) from January 1, 2020, to December 31, 2021. We invited 1360 individuals registered as contacts on DRKS to participate in a web-based survey between May 10, 2022, and June 30, 2022.
Only 0.3% (5/1842) of data sharing commitments in DRKS included a plan to share blank CRFs. Survey results showed high support for CRF sharing. More than 70% of respondents (223/301) were willing to share their CRFs, and 83.7% (252/301) were interested in CRF reuse. The most frequently reported reason for CRF sharing was improvement of comparability and interpretability of patient data (244/301; 81.0%). The most frequently reported reason against CRF sharing was missing approval by the sponsor (160/301; 53.2%). Researchers conducting commercial trials were significantly less likely to share CRFs than those conducting noncommercial trials (63.3% vs. 76.2%, OR 0.54, 95% CI 0.32–0.92) and they were less likely to reuse CRFs (78.5% vs. 84.6%, OR 0.66, 95% CI 0.35–1.24). The most frequently mentioned prerequisite for publication of CRFs in a public registry was its trustworthiness (244/301, 81.1%).
Data sharing commitments in DRKS revealed a low awareness of CRF sharing. Survey results showed generally strong support for CRF sharing, including the willingness to publish CRFs in a public registry, although legal and practical barriers were identified.
Sharing patient health information and biospecimens can improve health outcomes and accelerate breakthroughs in medical research. But patients generally lack understanding of how their clinical data and biospecimens are used or commercialized for research. In this mixed methods project, we assessed the impact of communication materials on patient understanding, attitudes, and perceptions.
Michigan Medicine patients were recruited for a survey (n = 480) or focus group (n = 33) via a web-based research study portal. The survey assessed the impact of mode of communication about health data and biospecimen sharing (via an informational poster vs. a news article) on patient perceptions of privacy, transparency, comfort, respect, and trust. Focus groups provided in-depth qualitative feedback on three communication materials, including a poster, FAQ webpage, and a consent form excerpt.
Among survey respondents, the type of intervention (poster vs. news) made no statistically significant difference in its influence on any characteristic. However, 95% preferred that Michigan Medicine tell them about patient data and biospecimen research sharing versus hearing it from the news. Focus group participants provided additional insights, discussing values and perceptions of altruism and reciprocity, concerns about commercialization, privacy, and security; and the desire for consent, control, and transparency.
Developing our understanding of patient data-sharing practices and integrating patient preferences into health system policy, through this work and continued exploration, contributes to building infrastructure that can be used to support the development of a learning health system across hospital systems nationally.
This project demonstrates the feasibility of connecting medical imaging data and features, SARS-CoV-2 genome variants, with clinical data in the National Clinical Cohort Collaborative (N3C) repository to accelerate integrative research on detection, diagnosis, and treatment of COVID-19-related morbidities. The N3C curated a rich collection of aggregated and de-identified electronic health records (EHR) data of over 18 million patients, including 7.5 million COVID-positive patients, seen at hospitals across the United States. Medical imaging data and variant samples are important data modalities used in the study of COVID-19.
Imaging data and features are hosted on the Cancer Imaging Archive (TCIA), and sequenced variant samples are analyzed and stored at the NIH GenBank. The University of Arkansas for Medical Sciences (UAMS) published the first COVID-19 data set of 105 patients on TCIA and 37 patients on GenBank. We developed a process to link imaging and genomic variants and N3C EHR data through Privacy Preserving Record Linkage (PPRL) using de-identified cryptographic hashes to match records associated with the same individuals without using patient identifiers.
The PPRL techniques were piloted using clinical and imaging data sets provided by UAMS. Developed software components and processes executed properly, and linked data were returned and processed for visualization.
Linking across clinical data sources at the patient level provides opportunities to gain insights from data that may not be known otherwise. The PPRL prototype and the pilot serve as a model to link disparate and diverse data repositories to enhance clinical research.
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